The PASCAL-Context [1]} contains a total of 10,103 images, for the five tasks of edge detection (Edge), semantic segmentation (Seg.), human parts segmentation (H.Parts), surface normals (Norm.), and saliency detection (Sal.). The NYUD-v2 dataset [2]} is an indoor dataset which consists of RGB-D images of 464 indoor scenes. There are 795 images for training and 654 images for testing, both with annotation for semantic segmentation (Seg.) and monocular depth estimation (Depth).
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